Natural Language Processing Specialization

Natural Language Processing Specialization

What you will learn

  • Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, translate words, and use locality-sensitive hashing to approximate nearest neighbors.
  • Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.
  • Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.
  • Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering, and build chatbots. Models covered include T5, BERT, transformer, reformer, and more!

Skills you will gain

  • Sentiment Analysis
  • Transformers
  • Attention Models
  • Machine Translation
  • Word2vec
  • Word Embeddings
  • Locality-Sensitive Hashing
  • Vector Space Models
  • Parts-of-Speech Tagging
  • N-gram Language Models
  • Autocorrect
  • Sentiment with Neural Networks
  • Siamese Networks
  • Natural Language Generation
  • Named Entity Recognition (NER)
  • Reformer Models
  • Neural Machine Translation
  • Chatbots
  • T5 + BERT Models

Natural language processing is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.

In the Natural Language Processing (NLP) Specialization, you will learn how to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages, summarize text, and even build chatbots. These and other NLP applications will be at the forefront of the coming transformation to an AI-powered future.

NLP is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.

  • 4 Courses
  • >4 months (6 hours/week)
  • Intermediate



Younes Bensouda Mourri

Younes Bensouda Mourri

Instructor of AI, Stanford University
Łukasz Kaiser

Łukasz Kaiser

Staff Research Scientist, Google Brain; Chargé de Recherche, CNRS
Eddy Shyu

Eddy Shyu

Curriculum Developer
Product Lead, DeepLearning.AI

Course Slides

You can download the annotated version of the course slides below.

*Note: The slides might not reflect the latest course video slides. Please refer to the lecture videos for the most up-to-date information. We encourage you to make your own notes.

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